
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMSNForImageClassification, ViTMSNModel
						from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor






class        __lowercase		:


					def __init__(						self							,							a__							,							a__=1_3							,							a__=3_0							,							a__=2							,							a__=3							,							a__=True							,							a__=True							,							a__=3_2							,							a__=5							,							a__=4							,							a__=3_7							,							a__="gelu"							,							a__=0.1							,							a__=0.1							,							a__=1_0							,							a__=0.02							,							a__=None							,							)		->				Any:





									'''simple docstring'''
									A_													=	parent
									A_													=	batch_size
									A_													=	image_size
									A_													=	patch_size
									A_													=	num_channels
									A_													=	is_training
									A_													=	use_labels
									A_													=	hidden_size
									A_													=	num_hidden_layers
									A_													=	num_attention_heads
									A_													=	intermediate_size
									A_													=	hidden_act
									A_													=	hidden_dropout_prob
									A_													=	attention_probs_dropout_prob
									A_													=	type_sequence_label_size
									A_													=	initializer_range
									A_													=	scope

									# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									A_													=	(image_size // patch_size) ** 2
									A_													=	num_patches + 1


					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									A_													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

									A_													=	None
									if self.use_labels:
													A_													=	ids_tensor([self.batch_size]							,							self.type_sequence_label_size    )

									A_													=	self.get_config()

									return config, pixel_values, labels


					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									return ViTMSNConfig(
									    image_size=self.image_size							,							patch_size=self.patch_size							,							num_channels=self.num_channels							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							initializer_range=self.initializer_range							,							)


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__    )		->				Dict:





									'''simple docstring'''
									A_													=	ViTMSNModel(config=UpperCamelCase__    )
									model.to(UpperCamelCase__    )
									model.eval()
									A_													=	model(UpperCamelCase__    )
									self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)    )


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__    )		->				str:





									'''simple docstring'''
									A_													=	self.type_sequence_label_size
									A_													=	ViTMSNForImageClassification(UpperCamelCase__    )
									model.to(UpperCamelCase__    )
									model.eval()
									A_													=	model(UpperCamelCase__							,							labels=UpperCamelCase__    )
									print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''    )
									print('''Labels: {labels}'''    )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)    )

									# test greyscale images
									A_													=	1
									A_													=	ViTMSNForImageClassification(UpperCamelCase__    )
									model.to(UpperCamelCase__    )
									model.eval()

									A_													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
									A_													=	model(UpperCamelCase__    )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)    )


					def 							lowerCAmelCase_     (						self    )		->				Optional[Any]:





									'''simple docstring'''
									A_													=	self.prepare_config_and_inputs()
									A_						,       A_						,       A_													=	config_and_inputs
									A_													=	{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class        __lowercase		(       _A				,							_A				,							unittest.TestCase							):
					__magic_name__    :				str  						=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					__magic_name__    :				Optional[int]  						=						(
					    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					__magic_name__    :				Optional[int]  						=						False
					__magic_name__    :				Optional[int]  						=						False
					__magic_name__    :				Tuple  						=						False
					__magic_name__    :				List[Any]  						=						False


					def 							lowerCAmelCase_     (						self    )		->				str:





									'''simple docstring'''
									A_													=	ViTMSNModelTester(self    )
									A_													=	ConfigTester(self							,							config_class=UpperCamelCase__							,							has_text_modality=UpperCamelCase__							,							hidden_size=3_7    )


					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									self.config_tester.run_common_tests()


					@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''    )
					def 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





									'''simple docstring'''
									pass


					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									A_						,       A_													=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													A_													=	model_class(UpperCamelCase__    )
													self.assertIsInstance(model.get_input_embeddings()							,							(nn.Module)    )
													A_													=	model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(UpperCamelCase__							,							nn.Linear    )    )


					def 							lowerCAmelCase_     (						self    )		->				List[str]:





									'''simple docstring'''
									A_						,       A_													=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													A_													=	model_class(UpperCamelCase__    )
													A_													=	inspect.signature(model.forward    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													A_													=	[*signature.parameters.keys()]

													A_													=	['''pixel_values''']
													self.assertListEqual(arg_names[:1]							,							UpperCamelCase__    )


					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									A_													=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase__    )


					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									A_													=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__    )


					@slow
					def 							lowerCAmelCase_     (						self    )		->				List[str]:





									'''simple docstring'''






									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_													=	ViTMSNModel.from_pretrained(UpperCamelCase__    )
													self.assertIsNotNone(UpperCamelCase__    )



def 							lowerCamelCase_	( ):
				A_													=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
				return image






@require_torch
@require_vision
class        __lowercase		(       unittest.TestCase							):


					@cached_property
					def 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





									'''simple docstring'''
									return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''    ) if is_vision_available() else None


					@slow
					def 							lowerCAmelCase_     (						self    )		->				List[Any]:





									'''simple docstring'''
									torch.manual_seed(2    )
									A_													=	ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''    ).to(UpperCamelCase__    )

									A_													=	self.default_image_processor
									A_													=	prepare_img()
									A_													=	image_processor(images=UpperCamelCase__							,							return_tensors='''pt'''    ).to(UpperCamelCase__    )

									# forward pass
									with torch.no_grad():
													A_													=	model(**UpperCamelCase__    )

									# verify the logits
									A_													=	torch.Size((1, 1_0_0_0)    )
									self.assertEqual(outputs.logits.shape							,							UpperCamelCase__    )

									A_													=	torch.tensor([-0.08_03, -0.44_54, -0.23_75]    ).to(UpperCamelCase__    )

									self.assertTrue(torch.allclose(outputs.logits[0, :3]							,							UpperCamelCase__							,							atol=1E-4    )    )

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase     :	Dict       			=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						None
  a__       			=						None







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""train"""
  a__       			=						"""dev"""
  a__       			=						"""test"""







class 			UpperCAmelCase_     :


  '''simple docstring'''







  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Union[Split, str]       )     ->      List[InputExample]:







    """simple docstring"""






    raise NotImplementedError
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    raise NotImplementedError





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[InputExample]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Tuple="[CLS]"     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					str="[SEP]"     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					List[str]=0     ,				UpperCamelCase__     :					str=0     ,				UpperCamelCase__     :					Optional[Any]=-100     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Optional[int]=True     ,				)     ->      List[InputFeatures]:







    """simple docstring"""






    __magic_name__								=			{label: i for i, label in enumerate(UpperCamelCase__       )}

    __magic_name__								=			[]
    for ex_index, example in enumerate(UpperCamelCase__       ):
      if ex_index % 1_0000 == 0:
        logger.info("""Writing example %d of %d"""     ,				UpperCamelCase__     ,				len(UpperCamelCase__       )       )

      __magic_name__								=			[]
      __magic_name__								=			[]
      for word, label in zip(example.words     ,				example.labels       ):
        __magic_name__								=			tokenizer.tokenize(UpperCamelCase__       )

        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
        if len(UpperCamelCase__       ) > 0:
          tokens.extend(UpperCamelCase__       )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      __magic_name__								=			tokenizer.num_special_tokens_to_add()
      if len(UpperCamelCase__       ) > max_seq_length - special_tokens_count:
        __magic_name__								=			tokens[: (max_seq_length - special_tokens_count)]
        __magic_name__								=			label_ids[: (max_seq_length - special_tokens_count)]

      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
        # roberta uses an extra separator b/w pairs of sentences
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
      __magic_name__								=			[sequence_a_segment_id] * len(UpperCamelCase__       )

      if cls_token_at_end:
        tokens += [cls_token]
        label_ids += [pad_token_label_id]
        segment_ids += [cls_token_segment_id]
      else:
        __magic_name__								=			[cls_token] + tokens
        __magic_name__								=			[pad_token_label_id] + label_ids
        __magic_name__								=			[cls_token_segment_id] + segment_ids

      __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      __magic_name__								=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__       )

      # Zero-pad up to the sequence length.
      __magic_name__								=			max_seq_length - len(UpperCamelCase__       )
      if pad_on_left:
        __magic_name__								=			([pad_token] * padding_length) + input_ids
        __magic_name__								=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
        __magic_name__								=			([pad_token_segment_id] * padding_length) + segment_ids
        __magic_name__								=			([pad_token_label_id] * padding_length) + label_ids
      else:
        input_ids += [pad_token] * padding_length
        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
        segment_ids += [pad_token_segment_id] * padding_length
        label_ids += [pad_token_label_id] * padding_length

      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length

      if ex_index < 5:
        logger.info("""*** Example ***"""       )
        logger.info("""guid: %s"""     ,				example.guid       )
        logger.info("""tokens: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in tokens]       )       )
        logger.info("""input_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_ids]       )       )
        logger.info("""input_mask: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_mask]       )       )
        logger.info("""segment_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in segment_ids]       )       )
        logger.info("""label_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in label_ids]       )       )

      if "token_type_ids" not in tokenizer.model_input_names:
        __magic_name__								=			None

      features.append(
          InputFeatures(
              input_ids=UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				token_type_ids=UpperCamelCase__     ,				label_ids=UpperCamelCase__       )       )
    return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset




    class 			UpperCAmelCase_     (					_A	):


      '''simple docstring'''







      a__       			=						42
      a__       			=						nn.CrossEntropyLoss().ignore_index
      def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      str:







        """simple docstring"""






        __magic_name__								=			os.path.join(
            UpperCamelCase__     ,				"""cached_{}_{}_{}""".format(mode.value     ,				tokenizer.__class__.__name__     ,				str(UpperCamelCase__       )       )     ,				)

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __magic_name__								=			cached_features_file + """.lock"""
        with FileLock(UpperCamelCase__       ):
          if os.path.exists(UpperCamelCase__       ) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''       )
            __magic_name__								=			torch.load(UpperCamelCase__       )
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''       )
            __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            __magic_name__								=			token_classification_task.convert_examples_to_features(
                UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)
            logger.info(F'''Saving features into cached file {cached_features_file}'''       )
            torch.save(self.features     ,				UpperCamelCase__       )
      def __len__(					self     :					List[str]       )     ->      Any:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]


if is_tf_available():
    import tensorflow as tf




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						42
      a__       			=						-1_00
      def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      List[Any]:







        """simple docstring"""






        __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
        # TODO clean up all this to leverage built-in features of tokenizers
        __magic_name__								=			token_classification_task.convert_examples_to_features(
            UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)

        def gen():
          for ex in self.features:
            if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
            else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

        if "token_type_ids" not in tokenizer.model_input_names:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,				(
                  {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )     ,				)
        else:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,				(
                  {
                      """input_ids""": tf.TensorShape([None]       ),
                      """attention_mask""": tf.TensorShape([None]       ),
                      """token_type_ids""": tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )     ,				)
      def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







        """simple docstring"""






        __magic_name__								=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

        return self.dataset
      def __len__(					self     :					Tuple       )     ->      Tuple:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]

